The work of patient flow management: A grounded theory study of emergency nurses

INTRODUCTION

The current crisis of emergency department overcrowding demands novel approaches. Despite a growing body of patient flow literature, there is little understanding of the work of emergency nurses. This study explored how emergency nurses perform patient flow management.

METHODS

Constructivist grounded theory and situational analysis methodologies were used to examine the work of emergency nurses. Twenty-nine focus groups and interviews of 27 participants and 64 hours of participant observation across four emergency departments were conducted between August 2022 and February 2023. Data were analyzed using coding, constant comparative analysis, and memo-writing to identify emergent themes and develop a substantive theory.

FINDINGS

Patient flow management is the work of balancing department resources and patient care to promote collective patient safety. Patient safety arises when care is ethical, efficient, and appropriately weighs care timeliness and comprehensiveness. Emergency nurses use numerous patient flow management strategies that can be organized into five tasks: information gathering, continuous triage, resource management, throughput management, and care oversight.

CONCLUSION

Patient flow management is complex, cognitively demanding work. The central contribution of this paper is a theoretical model that reflects emergency nurses'conceptualizations, discourse, and priorities. This model lays the foundation for knowledge sharing, training, and practice improvement.

Visibility-based layout of a hospital unit - An optimization approach

A patient fall is one of the adverse events in an inpatient unit of a hospital that can lead to disability and/or mortality. The medical literature suggests that increased visibility of patients by unit nurses is essential to improve patient monitoring and, in turn, reduce falls. However, such research has been descriptive in nature and does not provide an understanding of the characteristics of an optimal inpatient unit layout from a visibility-standpoint. To fill this gap, we adopt an interdisciplinary approach that combines the human field of view with facility layout design approaches. Specifically, we propose a bi-objective optimization model that jointly determines the optimal (i) location of a nurse in a nursing station and (ii) orientation of a patient's bed in a room for a given layout. The two objectives are maximizing the total visibility of all patients across patient rooms and minimizing inequity in visibility among those patients. We consider three different layout types, L-shaped, I-shaped, and Radial; these shapes exhibit the section of an inpatient unit that a nurse oversees. To estimate visibility, we employ the ray casting algorithm to quantify the visible target in a room when viewed by the nurse from the nursing station. The algorithm considers nurses' horizontal visual field and their depth of vision. Owing to the difficulty in solving the bi-objective model, we also propose a Multi-Objective Particle Swarm Optimization (MOPSO) heuristic to find (near) optimal solutions. Our findings suggest that the Radial layout appears to outperform the other two layouts in terms of the visibility-based objectives. We found that with a Radial layout, there can be an improvement of up to 50% in equity measure compared to an I-shaped layout. Similar improvements were observed when compared to the L-shaped layout as well. Further, the position of the patient's bed plays a role in maximizing the visibility of the patient's room. Insights from our work will enable understanding and quantifying the relationship between a physical layout and the corresponding provider-to-patient visibility to reduce adverse events.

An optimization framework for large-scale screening under limited testing capacity with application to COVID-19

We consider the problem of targeted mass screening of heterogeneous populations under limited testing capacity. Mass screening is an essential tool that arises in various settings, e.g., ensuring a safe supply of blood, reducing prevalence of sexually transmitted diseases, and mitigating the spread of infectious disease outbreaks. The goal of mass screening is to classify whole population groups as positive or negative for an infectious disease as efficiently and accurately as possible. Under limited testing capacity, it is not possible to screen the entire population and hence administrators must reserve testing and target those among the population that are most in need or most susceptible. This paper addresses this decision problem by taking advantage of accessible population-level risk information to identify the optimal set of sub-populations to target for screening. We conduct a comprehensive analysis that considers the two most commonly adopted schemes: Individual testing and Dorfman group testing. For both schemes, we formulate an optimization model that aims to minimize the number of misclassifications under a testing capacity constraint. By analyzing the formulations, we establish key structural properties which we use to construct efficient and accurate solution techniques. We conduct a case study on COVID-19 in the United States using geographic-based data. Our results reveal that the considered proactive targeted schemes outperform commonly adopted practices by substantially reducing misclassifications. Our case study provides important managerial insights with regards to optimal allocation of tests, testing designs, and protocols that dictate the optimality of schemes. Such insights can inform policy-makers with tailored and implementable data-driven recommendations.

Using simulation modelling to transform hospital planning and management to address health inequalities

Health inequalities are a perennial concern for policymakers and in service delivery to ensure fair and equitable access and outcomes. As health inequalities are socially influenced by employment, income, and education, this impacts healthcare services among socio-economically disadvantaged groups, making it a pertinent area for investigation in seeking to promote equitable access. Researchers widely acknowledge that health equity is a multi-faceted problem requiring approaches to understand the complexity and interconnections in hospital planning as a precursor to healthcare delivery. Operations research offers the potential to develop analytical models and frameworks to aid in complex decision-making that has both a strategic and operational function in problem-solving. This paper develops a simulation-based modelling framework (SimulEQUITY) to model the complexities in addressing health inequalities at a hospital level. The model encompasses an entire hospital operation (including inpatient, outpatient, and emergency department services) using the discrete-event simulation method to simulate the behaviour and performance of real-world systems, processes, or organisations. The paper makes a sustained contribution to knowledge by challenging the existing population-level planning approaches in healthcare that often overlook individual patient needs, especially within disadvantaged groups. By holistically modelling an entire hospital, socio-economic variations in patients' pathways are developed by incorporating individual patient attributes and variables. This innovative framework facilitates the exploration of diverse scenarios, from processes to resources and environmental factors, enabling key decision-makers to evaluate what intervention strategies to adopt as well as the likely scenarios for future patterns of healthcare inequality. The paper outlines the decision-support toolkit developed and the practical application of the SimulEQUITY model through to implementation within a hospital in the UK. This moves hospital management and strategic planning to a more dynamic position where a software-based approach, incorporating complexity, is implicit in the modelling rather than simplification and generalisation arising from the use of population-based models.

Accessible location of mobile labs for COVID-19 testing

In this study, we address the problem of finding the best locations for mobile labs offering COVID-19 testing. We assume that people within known demand centroids have a degree of mobility, i.e., they can travel a reasonable distance, and mobile labs have a limited-and-variable service area. Thus, we define a location problem concerned with optimizing a measure representing the accessibility of service to its potential clients. In particular, we use the concepts of classical, gradual, and cooperative coverage to define a weighted sum of multiple accessibility indicators. We formulate our optimization problem via a mixed-integer linear program which is intractable by commercial solvers for large instances. In response, we designed a Biased Random-Key Genetic Algorithm to solve the defined problem; this is capable of obtaining high-quality feasible solutions over large numbers of instances in seconds. Moreover, we present insights derived from a case study into the locations of COVID-19 testing mobile laboratories in Nuevo Leon, Mexico. Our experimental results show that our optimization approach can be used as a diagnostic tool to determine the number of mobile labs needed to satisfy a set of demand centroids, assuming that users have reduced mobility due to the restrictions because of the pandemic.

Scheduling mobile dental clinics: A heuristic approach considering fairness among school districts

Mobile dental clinics (MDCs) are suitable solutions for servicing people living in rural and urban areas that require dental healthcare. MDCs can provide dental care to the most vulnerable high-school students. However, scheduling MDCs to visit patients is critical to developing efficient dental programs. Here, we study a mobile dental clinic scheduling problem that arises from the real-life logistics management challenge faced by a school-based mobile dental care program in Southern Chile. This problem involves scheduling MDCs to treat high-school students at public schools while considering a fairness constraint among districts. Schools are circumscribed into districts, and by program regulations, at least 50% of the students in each district must receive dental care during the first semester. Fairness prevents some districts from waiting more time to receive dental care than others. We model the problem as a parallel machine scheduling problem with sequence-dependent setup costs and batch due dates and propose a mathematical model and a genetic algorithm-based solution to solve the problem. Our computational results demonstrate the effectiveness of our approaches in obtaining near-optimal solutions. Finally, dental program managers can use the methodologies presented in this work to schedule mobile dental clinics and improve their operations.

Heterogeneous donor circles for fair liver transplant allocation

The United States (U.S.) Department of Health and Human Services is interested in increasing geographical equity in access to liver transplant. The geographical disparity in the U.S. is fundamentally an outcome of variation in the organ supply to patient demand (s/d) ratios across the country (which cannot be treated as a single unit due to its size). To design a fairer system, we develop a nonlinear integer programming model that allocates the organ supply in order to maximize the minimum s/d ratios across all transplant centers. We design circular donation regions that are able to address the issues raised in legal challenges to earlier organ distribution frameworks. This allows us to reformulate our model as a set-partitioning problem. Our policy can be viewed as a heterogeneous donor circle policy, where the integer program optimizes the radius of the circle around each donation location. Compared to the current policy, which has fixed radius circles around donation locations, the heterogeneous donor circle policy greatly improves both the worst s/d ratio and the range between the maximum and minimum s/d ratios. We found that with the fixed radius policy of 500 nautical miles (NM), the s/d ratio ranges from 0.37 to 0.84 at transplant centers, while with the heterogeneous circle policy capped at a maximum radius of 500 NM, the s/d ratio ranges from 0.55 to 0.60, closely matching the national s/d ratio average of 0.5983. Our model matches the supply and demand in a more equitable fashion than existing policies and has a significant potential to improve the liver transplantation landscape.

Hospital food management: a multi-objective approach to reduce waste and costs

Food waste contributes significantly to greenhouse emissions and represents a substantial portion of overall waste within hospital facilities. Furthermore, uneaten food leads to a diminished nutritional intake for patients, that typically are vulnerable and ill. Therefore, this study developed mathematical models for constructing patient meals in a 1000-bed hospital located in Florida. The objective is to minimize food waste and meal-building costs while ensuring that the prepared meals meet the required nutrients and caloric content for patients. To accomplish these objectives, four mixed-integer programming models were employed, incorporating binary and continuous variables. The first model establishes a baseline for how the system currently works. This model generates the meals without minimizing waste or cost. The second model minimizes food waste, reducing waste up to 22.53 % compared to the baseline. The third model focuses on minimizing meal-building costs and achieves a substantial reduction of 37 %. Finally, a multi-objective optimization model was employed to simultaneously reduce both food waste and cost, resulting in reductions of 19.70 % in food waste and 32.66 % in meal-building costs. The results demonstrate the effectiveness of multi-objective optimization in reducing waste and costs within large-scale food service operations.

The potential of patient-based nurse staffing - a queuing theory application in the neonatal intensive care setting

Faced by a severe shortage of nurses and increasing demand for care, hospitals need to optimally determine their staffing levels. Ideally, nurses should be staffed to those shifts where they generate the highest positive value for the quality of healthcare. This paper develops an approach that identifies the incremental benefit of staffing an additional nurse depending on the patient mix. Based on the reasoning that timely fulfillment of care demand is essential for the healthcare process and its quality in the critical care setting, we propose to measure the incremental benefit of staffing an additional nurse through reductions in time until care arrives (TUCA). We determine TUCA by relying on queuing theory and parametrize the model with real data collected through an observational study. The study indicates that using the TUCA concept and applying queuing theory at the care event level has the potential to improve quality of care for a given nurse capacity by efficiently trading situations of high versus low workload.

Optimizing the management of electrophysiology labs in Chinese hospitals using a discrete event simulation tool

BACKGROUND

The growing demand for electrophysiology (EP) treatment in China presents a challenge for current EP care delivery systems. This study constructed a discrete event simulation (DES) model of an inpatient EP care delivery process, simulating a generalized inpatient journey of EP patients from admission to discharge in the cardiology department of a tertiary hospital in China. The model shows how many more patients the system can serve under different resource constraints by optimizing various phases of the care delivery process.

METHODS

Model inputs were based on and validated using real-world data, simulating the scheduling of limited resources among competing demands from different patient types. The patient stay consists of three stages, namely: the pre-operative stay, the EP procedure, and the post-operative stay. The model outcome was the total number of discharges during the simulation period. The scenario analysis presented in this paper covers two capacity-limiting scenarios (CLS): (1) fully occupied ward beds and (2) fully occupied electrophysiology laboratories (EP labs). Within each CLS, we investigated potential throughput when the length of stay or operative time was reduced by 10%, 20%, and 30%. The reductions were applied to patients with atrial fibrillation, the most common indication accounting for almost 30% of patients.

RESULTS

Model validation showed simulation results approximated actual data (137.2 discharges calculated vs. 137 observed). With fully occupied wards, reducing pre- and/or post-operative stay time resulted in a 1-7% increased throughput. With fully occupied EP labs, reduced operative time increased throughput by 3-12%.

CONCLUSIONS

Model validation and scenario analyses demonstrated that the DES model reliably reflects the EP care delivery process. Simulations identified which phases of the process should be optimized under different resource constraints, and the expected increases in patients served.

Select, route and schedule: optimizing community paramedicine service delivery with mandatory visits and patient prioritization

Healthcare delivery in the United States has been characterized as overly reactive and dependent on emergency department care for safety net coverage, with opportunity for improvement around discharge planning and high readmissions and emergency department bounce-back rates. Community paramedicine is a recent healthcare innovation that enables proactive visitation of patients at home, often shortly after emergency department and hospital discharge. We establish the first optimization-based framework to study efficiencies in the management and operation of a community paramedicine program. The collective innovations of our modeling include i) a novel hierarchical objective function with the goals of fairly increasing patient welfare, lowering hospital costs, and reducing readmissions and emergency department visits, ii) a new constraint set that ensures priority same-day visits for emergent patients, and iii) a further extension of our model to determine the minimum supplemental resources necessary to ensure feasibility in a single optimization formulation. Our medical-need based objective function prioritizes patients based on their clinical features and seeks to select and schedule patient visits and route healthcare providers to maximize overall patient welfare while favoring shorter tours. We use our methods to develop managerial insights via computational experiments on a variety of test instances based on real data from a hospital system in Upstate New York. We are able to identify optimal and nearly optimal tours that efficiently select, route, and schedule patients in reasonable timeframes. Our results lead to insights that can support managerial decisions about establishing (and improving existing) community paramedicine programs.

The assignment-dial-a-ride-problem

In this paper, we present the first Assignment-Dial-A-Ride problem motivated by a real-life problem faced by medico-social institutions in France. Every day, disabled people use ride-sharing services to go to an appropriate institution where they receive personal care. These institutions have to manage their staff to meet the demands of the people they receive. They have to solve three interconnected problems: the routing for the ride-sharing services; the assignment of disabled people to institutions; and the staff size in the institutions. We formulate a general Assignment-Dial-A-Ride problem to solve all three at the same time. We first present a matheuristic that iteratively generates routes using a large neighborhood search in which these routes are selected with a mixed integer linear program. After being validated on two special cases in the literature, the matheuristic is applied to real instances in three different areas in France. Several managerial results are derived. In particular, it is found that the amount of cost reduction induced by the people assignment is equivalent to the amount of cost reduction induced by the sharing of vehicles between institutions.

Surgical scheduling via optimization and machine learning with long-tailed data : Health care management science, in press

Using data from cardiovascular surgery patients with long and highly variable post-surgical lengths of stay (LOS), we develop a modeling framework to reduce recovery unit congestion. We estimate the LOS and its probability distribution using machine learning models, schedule procedures on a rolling basis using a variety of optimization models, and estimate performance with simulation. The machine learning models achieved only modest LOS prediction accuracy, despite access to a very rich set of patient characteristics. Compared to the current paper-based system used in the hospital, most optimization models failed to reduce congestion without increasing wait times for surgery. A conservative stochastic optimization with sufficient sampling to capture the long tail of the LOS distribution outperformed the current manual process and other stochastic and robust optimization approaches. These results highlight the perils of using oversimplified distributional models of LOS for scheduling procedures and the importance of using optimization methods well-suited to dealing with long-tailed behavior.

Patient-to-nurse ratios: Balancing quality, nurse turnover, and cost

We consider the problem of setting appropriate patient-to-nurse ratios in a hospital, an issue that is both complex and widely debated. There has been only limited effort to take advantage of the extensive empirical results from the medical literature to help construct analytical decision models for developing upper limits on patient-to-nurse ratios that are more patient- and nurse-oriented. For example, empirical studies have shown that each additional patient assigned per nurse in a hospital is associated with increases in mortality rates, length-of-stay, and nurse burnout. Failure to consider these effects leads to disregarded potential cost savings resulting from providing higher quality of care and fewer nurse turnovers. Thus, we present a nurse staffing model that incorporates patient length-of-stay, nurse turnover, and costs related to patient-to-nurse ratios. We present results based on data collected from three participating hospitals, the American Hospital Association (AHA), and the California Office of Statewide Health Planning and Development (OSHPD). By incorporating patient and nurse outcomes, we show that lower patient-to-nurse ratios can potentially provide hospitals with financial benefits in addition to improving the quality of care. Furthermore, our results show that higher policy patient-to-nurse ratio upper limits may not be as harmful in smaller hospitals, but lower policy patient-to-nurse ratios may be necessary for larger hospitals. These results suggest that a "one ratio fits all" patient-to-nurse ratio is not optimal. A preferable policy would be to allow the ratio to be hospital-dependent.

Managing a multi-panel clinic with heterogeneous patients

Primary care providers (PCPs) are considered the first-line defenders in preventive care. Patients seeking service from the same PCP constitute that physician's panel, which determines the overall supply and demand of the physician. The process of allocating patients to physician panels is called panel design. This study quantifies patient overflow and builds a mathematical model to evaluate the effect of two implementable panel assignments. In specialized panel assignment, patients are assigned based on their medical needs or visit frequency. In equal panel assignment, patients are distributed uniformly to maintain a similar composition across panels. We utilize majorization theory and numerical examples to evaluate the performance of the two designs. The results show that specialized panel assignment outperforms when (1) patient demands and physician capacity are relatively balanced or (2) patients who require frequent visits incur a higher shortage penalty. In a simulation model with actual patient arrival patterns, we also illustrate the robustness of the results and demonstrate the effect of switching panel policy when the patient pool changes over time.

Determining optimal COVID-19 testing center locations and capacities

We study the problem of determining the locations and capacities of COVID-19 specimen collection centers to efficiently improve accessibility to polymerase chain reaction testing during surges in testing demand. We develop a two-echelon multi-period location and capacity allocation model that determines optimal number and locations of pop-up testing centers, capacities of the existing centers as well as assignments of demand regions to these centers, and centers to labs. The objective is to minimize the total number of delayed appointments and specimens subject to budget, capacity, and turnaround time constraints, which will in turn improve the accessibility to testing. We apply our model to a case study for locating COVID-19 testing centers in the Region of Waterloo, Canada using data from the Ontario Ministry of Health, public health databases, and medical literature. We also test the performance of the model under uncertain demand and analyze its outputs under various scenarios. Our analyses provide practical insights to the public health decision-makers on the timing of capacity expansions and the locations for the new pop-up centers. According to our results, the optimal strategy is to dynamically expand the existing specimen collection center capacities and prevent bottlenecks by locating pop-up facilities. The optimal locations of pop-ups are among the densely populated areas that are in proximity to the lab and a subset of those locations are selected with the changes in demand. A comparison with a static approach promises up to 39% cost savings under high demand using the developed multi-period model.

Combining machine learning and optimization for the operational patient-bed assignment problem

Assigning inpatients to hospital beds impacts patient satisfaction and the workload of nurses and doctors. The assignment is subject to unknown inpatient arrivals, in particular for emergency patients. Hospitals, therefore, need to deal with uncertainty on actual bed requirements and potential shortage situations as bed capacities are limited. This paper develops a model and solution approach for solving the patient bed-assignment problem that is based on a machine learning (ML) approach to forecasting emergency patients. First, it contributes by improving the anticipation of emergency patients using ML approaches, incorporating weather data, time and dates, important local and regional events, as well as current and historical occupancy levels. Drawing on real-life data from a large case hospital, we were able to improve forecasting accuracy for emergency inpatient arrivals. We achieved up to 17% better root mean square error (RMSE) when using ML methods compared to a baseline approach relying on averages for historical arrival rates. We further show that the ML methods outperform time series forecasts. Second, we develop a new hyper-heuristic for solving real-life problem instances based on the pilot method and a specialized greedy look-ahead (GLA) heuristic. When applying the hyper-heuristic in test sets we were able to increase the objective function by up to 5.3% in comparison to the benchmark approach in [40]. A benchmark with a Genetic Algorithm shows also the superiority of the hyper-heuristic. Third, the combination of ML for emergency patient admission forecasting with advanced optimization through the hyper-heuristic allowed us to obtain an improvement of up to 3.3% on a real-life problem.

Food bank operations: review of operation research methods and challenges during COVID-19

Food banks have played a crucial role in mitigating food insecurity in affluent countries for over four decades. Throughout the years, academics have researched food banks for a variety of operational problems, resulting in several research papers on the topic. However, despite significant academic interest, the operational challenges and optimization of food bank operations remain under-researched. This study aims to conduct a systematic literature review on food bank operations and provide evidence-based recommendations for addressing prevalent challenges, and provide decision-makers with practical recommendations. In addition, this investigation seeks to investigate the impact of the COVID-19 pandemic on food bank operations. We conducted a comprehensive analysis of academic publications on food bank operations using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) in order to get a deeper comprehension of the problems confronting food bank operations. Using a keyword search strategy with the logical operators "AND" and "OR," two search methods were utilized to identify relevant articles on food bank operations management, supply chain, distribution, and production in our first search. In our second search, we discovered articles in the "Operations Research & Management Science" (OR &MS) category of Web of Science containing food bank-related keywords such as food charity, food donation, and food aid. The database searches yielded 246 hits, and the article content was scanned to eliminate irrelevant articles by removing non-English articles and duplicated studies, leaving 55 articles for further examination. Our extensive examination of Operations Research (OR) methodologies reveals that Mixed-Integer Linear Programming (MILP) models are the most commonly used methodology, followed by Linear Program (LP), Dynamic Program (DP), and Data Envelopment Analysis (DEA) techniques. The key findings of this study emphasize the operational challenges food banks encountered during and after the COVID-19 pandemic, including supply chain disruptions, increased demand, and volunteer shortages. To address these issues, effective solutions, including the management of food donations and volunteer scheduling, were proposed. Our findings have practical implications for decision-makers in food bank management, highlighting the importance of adopting evidence-based solutions. Finally, Limitations and prospective research directions in food bank management are discussed, with an emphasis on the need for ongoing research in this crucial area.

Intraday dynamic rescheduling under patient no-shows

Patient no-shows are a major source of uncertainty for outpatient clinics. A common approach to hedge against the effect of no-shows is to overbook. The trade-off between patient's waiting costs and provider idling/overtime costs determines the optimal level of overbooking. Existing work on appointment scheduling assumes that appointment times cannot be updated once they have been assigned. However, advances in communication technology and the adoption of online (as opposed to in-person) appointments make it possible for appointments to be flexible. In this paper, we describe an intraday dynamic rescheduling model that adjusts upcoming appointments based on observed no-shows. We formulate the problem as a Markov Decision Process in order to compute the optimal pre-day schedule and the optimal policy to update the schedule for every scenario of no-shows. We also propose an alternative formulation based on the idea of 'atomic' actions that allows us to apply a shortest path algorithm to solve for the optimal policy more efficiently. Based on a numerical study using parameter estimates from existing literature, we find that intraday dynamic rescheduling can reduce expected cost by 15% compared to static scheduling.

Health information exchange network under collaboration, cooperation, and competition: A game-theoretic approach

Health Information Exchange (HIE) network allows securely accessing and sharing healthcare-related information among healthcare providers (HCPs) and payers. HIE services are provided by a non-profit/profit organizations under several subscription plans options. A few studies have addressed the sustainability of the HIE network such that HIE providers, HCPs, and payers remain profitable in the long term. However, none of these studies addressed the coexistence of multiple HIE providers in the network. Such coexistence may have a huge impact on the behavior of healthcare systems in terms of adoption rate and HIE pricing strategies. In addition, in spite of all the effort to maintain cooperation between HIE providers, there is still a chance of competition among them in the market. Possible competition among service providers leads to many concerns about the HIE network sustainability and behavior. In this study, a game-theoretic approach to model the HIE market is proposed. Game-theory is used to simulate the behavior of the three different HIE network agents in the HIE market: HIE providers, HCPs, and payers. Pricing strategies and adoption decisions are optimized using a Linear Programming (LP) mathematical model. Results show that the relation between HIEs in the market is crucial to HCP/Payer adoption decision specially to small HCPs. A small change in the discount rate proposed by a competitive HIE provider will highly affect the decision of HCP/payers to join the HIE network. Finally, competition opened the opportunity for more HCPs to join the network due to reduced pricing. Furthermore, collaborative HIEs provided better performance compared to cooperative in terms of profit and HCP adoption rate by sharing their overall costs and revenues.

What makes a health system good? From cost-effectiveness analysis to ethical improvement in health systems

Fair allocation of scarce healthcare resources has been much studied within philosophy and bioethics, but analysis has focused on a narrow range of cases. The Covid-19 pandemic provided significant new challenges, making powerfully visible the extent to which health systems can be fragile, and how scarcities within crucial elements of interlinked care pathways can lead to cascading failures. Health system resilience, while previously a key topic in global health, can now be seen to be a vital concern in high-income countries too. Unfortunately, mainstream philosophical approaches to the ethics of rationing and prioritisation provide little guidance for these new problems of scarcity. Indeed, the cascading failures were arguably exacerbated by earlier attempts to make health systems leaner and more efficient. This paper argues that health systems should move from simple and atomistic approaches to measuring effectiveness to approaches that are holistic both in focusing on performance at the level of the health system as a whole, and also in incorporating a wider range of ethical concerns in thinking about what makes a health system good.

Managing surgical waiting lists through dynamic priority scoring

Prioritising elective surgery patients under the Australian three-category system is inherently subjective due to variability in clinician decision making and the potential for extraneous factors to influence category assignment. As a result, waiting time inequities can exist which may lead to adverse health outcomes and increased morbidity, especially for patients deemed to be low priority. This study investigated the use of a dynamic priority scoring (DPS) system to rank elective surgery patients more equitably, based on a combination of waiting time and clinical factors. Such a system enables patients to progress on the waiting list in a more objective and transparent manner, at a rate relative to their clinical need. Simulation results comparing the two systems indicate that the DPS system has potential to assist in managing waiting lists by standardising waiting times relative to urgency category, in addition to improving waiting time consistency for patients of similar clinical need. In clinical practice, this system is likely to reduce subjectivity, increase transparency, and improve overall efficiency of waiting list management by providing an objective metric to prioritise patients. Such a system is also likely to increase public trust and confidence in the systems used to manage waiting lists.

Optimization models for patient and technician scheduling in hemodialysis centers

Patient and technician scheduling problem in hemodialysis centers presents a unique setting in healthcare operations as (1) unlike other healthcare problems, dialysis appointments have a steady state and the treatment times are determined in advance of the appointments, and (2) once the appointments are set, technicians will have to be assigned to two types of jobs per appointment: putting on and taking off patients (connecting to and disconnecting from dialysis machines). In this study, we design a mixed-integer programming model to minimize technicians' operating costs (regular and overtime costs) at large-scale hemodialysis centers. As this formulation proves to be computationally challenging to solve, we propose a novel reformulation of the problem as a discrete-time assignment model and prove that the two formulations are equivalent under a specific condition. We then simulate instances based on the data from our collaborating hemodialysis center to evaluate the performance of our proposed formulations. We compare our results to the current scheduling policy at the center. In our numerical analysis, we reduced the technician operating costs by 17% on average (up to 49%) compared to the current practice. We further conduct a post-optimality analysis and develop a predictive model that can estimate the number of required technicians based on the center's attributes and patients' input variables. Our predictive model reveals that the optimal number of technicians is strongly related to the time flexibility of patients and their dialysis times. Our findings can help clinic managers at hemodialysis centers to accurately estimate the technician requirements.

Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients

Early bed assignments of elective surgical patients can be a useful planning tool for hospital staff; they provide certainty in patient placement and allow nursing staff to prepare for patients' arrivals to the unit. However, given the variability in the surgical schedule, they can also result in timing mismatches-beds remain empty while their assigned patients are still in surgery, while other ready-to-move patients are waiting for their beds to become available. In this study, we used data from four surgical units in a large academic medical center to build a discrete-event simulation with which we show how a Just-In-Time (JIT) bed assignment, in which ready-to-move patients are assigned to ready-beds, would decrease bed idle time and increase access to general care beds for all surgical patients. Additionally, our simulation demonstrates the potential synergistic effects of combining the JIT assignment policy with a strategy that co-locates short-stay surgical patients out of inpatient beds, increasing the bed supply. The simulation results motivated hospital leadership to implement both strategies across these four surgical inpatient units in early 2017. In the several months post-implementation, the average patient wait time decreased 25.0% overall, driven by decreases of 32.9% for ED-to-floor transfers (from 3.66 to 2.45 hours on average) and 37.4% for PACU-to-floor transfers (from 2.36 to 1.48 hours), the two major sources of admissions to the surgical floors, without adding additional capacity.

A reinforcement learning-based optimal control approach for managing an elective surgery backlog after pandemic disruption

Contagious disease pandemics, such as COVID-19, can cause hospitals around the world to delay nonemergent elective surgeries, which results in a large surgery backlog. To develop an operational solution for providing patients timely surgical care with limited health care resources, this study proposes a stochastic control process-based method that helps hospitals make operational recovery plans to clear their surgery backlog and restore surgical activity safely. The elective surgery backlog recovery process is modeled by a general discrete-time queueing network system, which is formulated by a Markov decision process. A scheduling optimization algorithm based on the piecewise decaying [Formula: see text]-greedy reinforcement learning algorithm is proposed to make dynamic daily surgery scheduling plans considering newly arrived patients, waiting time and clinical urgency. The proposed method is tested through a set of simulated dataset, and implemented on an elective surgery backlog that built up in one large general hospital in China after the outbreak of COVID-19. The results show that, compared with the current policy, the proposed method can effectively and rapidly clear the surgery backlog caused by a pandemic while ensuring that all patients receive timely surgical care. These results encourage the wider adoption of the proposed method to manage surgery scheduling during all phases of a public health crisis.

Benchmark dataset and instance generator for real-world three-dimensional bin packing problems

In this article, a benchmark for real-world bin packing problems is proposed. This dataset consists of 12 instances of varying levels of complexity regarding size (with the number of packages ranging from 38 to 53) and user-defined requirements. In fact, several real-world-oriented restrictions were taken into account to build these instances: i) item and bin dimensions, ii) weight restrictions, iii) affinities among package categories iv) preferences for package ordering and v) load balancing. Besides the data, we also offer an own developed Python script for the dataset generation, coined Q4RealBPP-DataGen. The benchmark was initially proposed to evaluate the performance of quantum solvers. Therefore, the characteristics of this set of instances were designed according to the current limitations of quantum devices. Additionally, the dataset generator is included to allow the construction of general-purpose benchmarks. The data introduced in this article provides a baseline that will encourage quantum computing researchers to work on real-world bin packing problems.

Real operational data for the concrete delivery problem

The data article describes a real operational dataset for the Concrete Delivery Problem (CDP). The dataset consists of 263 instances corresponding to daily orders of concrete from construction sites in Quebec, Canada. A concrete producer, i.e., a concrete-producing company that delivers concrete, provided the raw data. We cleaned the data by removing entries corresponding to non-complete orders. We processed these raw data to form instances useful for benchmarking optimization algorithms developed to solve the CDP. We also anonymized the published dataset by removing any client information and addresses corresponding to production or construction sites. The dataset is useful for researchers and practitioners studying the CDP. It can be processed to create artificial data for variations of the CDP. In its current form, the data contain information about intra-day orders. Thus, selected instances from the dataset are useful for CDP's dynamic aspect considering real-time orders.

Modelling interventions and contact networks to reduce the spread of carbapenem-resistant organisms between individuals in the ICU

BACKGROUND

Contact precautions are widely used to prevent the transmission of carbapenem-resistant organisms (CROs) in hospital wards. However, evidence for their effectiveness in natural hospital environments is limited.

OBJECTIVE

To determine which contact precautions, healthcare worker (HCW)-patient interactions, and patient and ward characteristics are associated with greater risk of CRO infection or colonization.

DESIGN, SETTING AND PARTICIPANTS

CRO clinical and surveillance cultures from two high-acuity wards were assessed through probabilistic modelling to characterize a susceptible patient's risk of CRO infection or colonization during a ward stay. User- and time-stamped electronic health records were used to build HCW-mediated contact networks between patients. Probabilistic models were adjusted for patient (e.g. antibiotic administration) and ward (e.g. hand hygiene compliance, environmental cleaning) characteristics. The effects of risk factors were assessed by adjusted odds ratio (aOR) and 95% Bayesian credible intervals (CrI).

EXPOSURES

The degree of interaction with CRO-positive patients, stratified by whether CRO-positive patients were on contact precautions.

MAIN OUTCOMES AND MEASURES

The prevalence of CROs and number of new carriers (i.e. incident CRO aquisition).

RESULTS

Among 2193 ward visits, 126 (5.8%) patients became colonized or infected with CROs. Susceptible patients had 4.8 daily interactions with CRO-positive individuals on contact precautions (vs 1.9 interactions with those not on contact precautions). The use of contact precautions for CRO-positive patients was associated with a reduced rate (7.4 vs 93.5 per 1000 patient-days at risk) and odds (aOR 0.03, 95% CrI 0.01-0.17) of CRO acquisition among susceptible patients, resulting in an estimated absolute risk reduction of 9.0% (95% CrI 7.6-9.2%). Also, carbapenem administration to susceptible patients was associated with increased odds of CRO acquisition (aOR 2.38, 95% CrI 1.70-3.29).

CONCLUSIONS AND RELEVANCE

In this population-based cohort study, the use of contact precautions for patients colonized or infected with CROs was associated with lower risk of CRO acquisition among susceptible patients, even after adjusting for antibiotic exposure. Further studies that include organism genotyping are needed to confirm these findings.

A two-stage stochastic optimization framework to allocate operating room capacity in publicly-funded hospitals under uncertainty

Surgery demand is an uncertain parameter in addressing the problem of surgery block allocations, and its typical variability should be considered to ensure the feasibility of surgical planning. We develop two models, a stochastic recourse programming model and a two-stage stochastic optimization (SO) model with incorporated risk measure terms in the objective functions to determine a planning decision that is made to allocate surgical specialties to operating rooms (ORs). Our aim is to minimize the costs associated with postponements and unscheduled demands as well as the inefficient use of OR capacity. The results of these models are compared using a case of a real-life hospital to determine which model better copes with uncertainty. We propose a novel framework to transform the SO model based on its deterministic counterpart. Three SO models are proposed with respect to the variability and infeasibility of the measures of the objective function to encode the construction of the SO framework. The analysis of the experimental results demonstrates that the SO model offers better performance under a highly volatile demand environment than the recourse model. The originality of this work lies in its use of SO transformation framework and its development of stochastic models to address the problem of surgery capacity allocation based on a real case.

A two-stage partial fixing approach for solving the residency block scheduling problem

We consider constructing feasible annual block schedules for residents in a medical training program. We must satisfy coverage requirements to guarantee an acceptable staffing level for different services in the hospital as well as education requirements to ensure residents receive appropriate training to pursue their individual (sub-)specialty interests. The complex requirement structure makes this resident block scheduling problem a complicated combinatorial optimization problem. Solving a conventional integer program formulation for certain practical instances directly using traditional solution techniques will result in unacceptably slow performance. To address this, we propose a partial fixing approach, which completes the schedule construction iteratively through two sequential stages. The first stage focuses on the resident assignments for a small set of predetermined services through solving a much smaller and easier problem relaxation, while the second stage completes the rest of the schedule construction after fixing those assignments specified by the first stage's solution. We develop cut generation mechanisms to prune off the bad decisions made by the first stage if infeasibility arises in the second stage. We additionally propose a network-based model to assist us with an effective service selection for the first stage to work on the corresponding resident assignments to achieve an efficient and robust performance of the proposed two-stage iterative approach. Experiments using real-world inputs from our clinical collaborator show that our approach can speed up the schedule construction at least 5 times for all instances and even over 100 times for some huge-size instances compared to applying traditional techniques directly.

Online scheduling using a fixed template: the case of outpatient chemotherapy drug administration

In this paper, we use a fixed template of slots for the online scheduling of appointments. The template is a link between planning the service capacity at a tactical level and online scheduling at an operational level. We develop a detailed heuristic for the case of drug administration appointments in outpatient chemotherapy. However, the approach can be applied to online scheduling in other application areas as well. The desired scheduling principles are incorporated into the cost coefficients of the objective function of a binary integer program for booking appointments in the template, as requests arrive. The day and time of appointments are decided simultaneously, rather than sequentially, where optimal solutions may be eliminated from the search. The service that we consider in this paper is an example to show the versatility of a fixed template online scheduling model. It requires two types of resource, one of which is exclusively assigned for the whole appointment duration, and the other is shared among multiple appointments after setting up the service. There is high heterogeneity among appointments on a day of this service. The appointments may range from fifteen minutes to more than eight hours. A fixed template gives a pattern for the scheduling of possibly required steps before the service. Instead of maximizing the fill-rate of the template, the objective of our heuristic is to have high performance in multiple indicators pertaining to various stakeholders (patients, nurses, and the clinic). By simulation, we illustrate the performance of the fixed template model for the key indicators.

Screening for preclinical Alzheimer's disease: Deriving optimal policies using a partially observable Markov model

Alzheimer's Disease (AD) is believed to be the most common type of dementia. Even though screening for AD has been discussed widely, there is no screening program implemented as part of a policy in any country. Current medical research motivates focusing on the preclinical stages of the disease in a modeling initiative. We develop a partially observable Markov decision process model to determine optimal screening programs. The model contains disease free and preclinical AD partially observable states and the screening decision is taken while an individual is in one of those states. An observable diagnosed preclinical AD state is integrated along with observable mild cognitive impairment, AD and death states. Transition probabilities among states are estimated using data from Knight Alzheimer's Disease Research Center (KADRC) and relevant literature. With an objective of maximizing expected total quality-adjusted life years (QALYs), the output of the model is an optimal screening program that specifies at what points in time an individual over 50 years of age with a given risk of AD will be directed to undergo screening. The screening test used to diagnose preclinical AD has a positive disutility, is imperfect and its sensitivity and specificity are estimated using the KADRC data set. We study the impact of a potential intervention with a parameterized effectiveness and disutility on model outcomes for three different risk profiles (low, medium and high). When intervention effectiveness and disutility are at their best, the optimal screening policy is to screen every year between ages 50 and 95, with an overall QALY gain of 0.94, 1.9 and 2.9 for low, medium and high risk profiles, respectively. As intervention effectiveness diminishes and/or its disutility increases, the optimal policy changes to sporadic screening and then to never screening. Under several scenarios, some screening within the time horizon is optimal from a QALY perspective. Moreover, an in-depth analysis of costs reveals that implementing these policies are either cost-saving or cost-effective.

Monitoring policy in the context of preventive treatment of cardiovascular disease

Preventing chronic diseases is an essential aspect of medical care. To prevent chronic diseases, physicians focus on monitoring their risk factors and prescribing the necessary medication. The optimal monitoring policy depends on the patient's risk factors and demographics. Monitoring too frequently may be unnecessary and costly; on the other hand, monitoring the patient infrequently means the patient may forgo needed treatment and experience adverse events related to the disease. We propose a finite horizon and finite-state Markov decision process to define monitoring policies. To build our Markov decision process, we estimate stochastic models based on longitudinal observational data from electronic health records for a large cohort of patients seen in the national U.S. Veterans Affairs health system. We use our model to study policies for whether or when to assess the need for cholesterol-lowering medications. We further use our model to investigate the role of gender and race on optimal monitoring policies.

Nurse rostering with fatigue modelling : Incorporating a validated sleep model with biological variations in nurse rostering

We use a real Nurse Rostering Problem and a validated model of human sleep to formulate the Nurse Rostering Problem with Fatigue. The fatigue modelling includes individual biologies, thus enabling personalised schedules for every nurse. We create an approximation of the sleep model in the form of a look-up table, enabling its incorporation into nurse rostering. The problem is solved using an algorithm that combines Mixed-Integer Programming and Constraint Programming with a Large Neighbourhood Search. A post-processing algorithm deals with errors, to produce feasible rosters minimising global fatigue. The results demonstrate the realism of protecting nurses from highly fatiguing schedules and ensuring the alertness of staff. We further demonstrate how minimally increased staffing levels enable lower fatigue, and find evidence to suggest biological complementarity among staff can be used to reduce fatigue. We also demonstrate how tailoring shifts to nurses' biology reduces the overall fatigue of the team, which means managers must grapple with the issue of fairness in rostering.

High level of infection prevention and control in surveyed hospitals in Colombia, 2021

Objective

This study aimed to determine the performance of infection prevention and control (IPC) programs in eight core components in level 2 and level 3 hospitals across all provinces in Colombia.

Methods

This cross-sectional study used self-assessed IPC performance data voluntarily reported by hospitals to the Ministry of Health and Social Protection during 2021. Each of the eight core components of the World Health Organization's checklist in the Infection Prevention and Control Assessment Framework contributes a maximum score of 100, and the overall IPC performance score is the sum of these component scores. IPC performance is graded according to the overall score as inadequate (0-200), basic (201-400), intermediate (401-600) or advanced (601-800).

Results

Of the 441 level 2 and level 3 hospitals, 267 (61%) reported their IPC performance. The median (interquartile range [IQR]) overall IPC score was 672 (IQR: 578-715). Of the 267 hospitals reporting, 187 (70%) achieved an advanced level of IPC. The median overall IPC score was significantly higher in private hospitals (690, IQR: 598-725) than in public hospitals (629, IQR: 538-683) (P < 0.001). Among the core components, scores were highest for the category assessing IPC guidelines (median score: 97.5) and lowest for the category assessing workload, staffing and bed occupancy (median score: 70). Median overall IPC scores varied across the provinces (P < 0.001).

Conclusions

This countrywide assessment showed that 70% of surveyed hospitals achieved a self-reported advanced level of IPC performance, which reflects progress in building health system resilience. Since only 61% of eligible hospitals participated, an important next step is to ensure the participation of all hospitals in future assessments.

Analysis of operations research methods for decision problems in the industrial symbiosis: a literature review

Industrial symbiosis (IS) is an approach that aims to use resources efficiently by cooperating between independent enterprises in raw materials, energy, and similar sectors. As a result of cooperation, businesses gain economic, environmental, and social benefits. Especially in recent years, IS applications have become widespread due to the problems experienced in the supply of resources. The presence of more than one enterprise in cooperation creates a complex network structure in IS applications. In this complex system, many decision problems are encountered in establishing and effectively maintaining the industrial symbiosis network. Operations research techniques are at the forefront of the methods used to solve decision problems. This study examined studies using operations research techniques in industrial symbiosis. Studies were divided into four classes according to the methods they used: exact methods, heuristic methods, multi-criteria decision-making, and simulation. In the literature review, the studies in the Web of Science (WOS) database are systematically presented by scanning with the determined keywords. As a result of the study, it was analyzed which method was preferred and where the methods could be applied in industrial symbiosis.

Incorporating medication therapy management into community pharmacy workflows

Medication Therapy Management (MTM) is a group of pharmacist-provided services that optimize individual patients' drug therapy outcomes. Since community pharmacies' primary business platform is the dispensing of medications, and providing MTM services is a secondary source of revenue, pharmacies with limited resources are operationally challenged when trying to efficiently deliver both types of services. To address this problem, we follow a queueing network approach to develop an operational model of a community pharmacy workflow. Through our model, we derive structural results to determine conditions for a pharmacy to achieve economies of scope when providing both prescription and MTM services. We also develop a process simulation to compare different scenarios according to our economies of scope model, varying in provided services, personnel, service demand, and other operational variables. Outcomes examined include profitability, service rate, and sensitivity of some operation variables to profitability. Based on our results, we provide practical insights to help community pharmacy administrators and healthcare policy makers in their decision process.

Quantile regression forests for individualized surgery scheduling

Determining the optimal surgical case start times is a challenging stochastic optimization problem that shares a key feature with many other healthcare operations problems. Namely, successful problem solutions require using a vast array of available historical data to create distributions that accurately capture a case duration's uncertainty for integration into an optimization model. Distribution fitting is the conventional approach to generate these distributions, but it can only employ a limited, aggregate portion of the detailed patient features available in Electronic Medical Records systems today. If all the available information can be taken advantage of, then distributions individualized to every case can be constructed whose precision would support higher quality solutions in the presence of uncertainty. Our individualized stochastic optimization framework shows how the quantile regression forest (QRF) method predicts individualized distributions that are integrable into sample-average approximation, robust optimization, and distributionally robust optimization models for problems like surgery scheduling. In this paper, we present some related theoretical performance guarantees for each formulation. Numerically, we also study our approach's benefits relative to three other traditional models using data from Memorial Sloan Kettering Cancer Center in New York, NY, USA.

Inverse optimization on hierarchical networks: an application to breast cancer clinical pathways

Clinical pathways are standardized processes that outline the steps required for managing a specific disease. However, patient pathways often deviate from clinical pathways. Measuring the concordance of patient pathways to clinical pathways is important for health system monitoring and informing quality improvement initiatives. In this paper, we develop an inverse optimization-based approach to measuring pathway concordance in breast cancer, a complex disease. We capture this complexity in a hierarchical network that models the patient's journey through the health system. A novel inverse shortest path model is formulated and solved on this hierarchical network to estimate arc costs, which are used to form a concordance metric to measure the distance between patient pathways and shortest paths (i.e., clinical pathways). Using real breast cancer patient data from Ontario, Canada, we demonstrate that our concordance metric has a statistically significant association with survival for all breast cancer patient subgroups. We also use it to quantify the extent of patient pathway discordances across all subgroups, finding that patients undertaking additional clinical activities constitute the primary driver of discordance in the population.

Decision-support systems for ambulatory care, including pandemic requirements: using mathematically optimized solutions

BACKGROUND

The healthcare sector poses many strategic, tactic and operational planning questions. Due to the historically grown structures, planning is often locally confined and much optimization potential is foregone.

METHODS

We implemented optimized decision-support systems for ambulatory care for four different real-world case studies that cover a variety of aspects in terms of planning scope and decision support tools. All are based on interactive cartographic representations and are being developed in cooperation with domain experts. The planning problems that we present are the problem of positioning centers for vaccination against Covid-19 (strategical) and emergency doctors (strategical/tactical), the out-of-hours pharmacy planning problem (tactical), and the route planning of patient transport services (operational). For each problem, we describe the planning question, give an overview of the mathematical model and present the implemented decision support application.

RESULTS

Mathematical optimization can be used to model and solve these planning problems. However, in order to convince decision-makers of an alternative solution structure, mathematical solutions must be comprehensible and tangible. Appealing and interactive decision-support tools can be used in practice to convince public health experts of the benefits of an alternative solution. The more strategic the problem and the less sensitive the data, the easier it is to put a tool into practice.

CONCLUSIONS

Exploring solutions interactively is rarely supported in existing planning tools. However, in order to bring new innovative tools into productive use, many hurdles must be overcome.

A multi-product multi-period stochastic model for a blood supply chain considering blood substitution and demand uncertainty

This paper presents a multi-product multi-period stochastic program for an integrated blood supply chain that considers red blood cells and platelets while accounting for multi-product interactions, demand uncertainty, blood age information, blood type substitution, and three types of patients. The aim is to minimize the total cost incurred during the collection, production, inventory, and distribution echelons under centralized control. The supply chains for red blood cells and platelets intertwine at the collection and production echelons as collected whole blood can be separated into red blood cells and platelets at the same time. By adapting to a real-world blood supply chain with one blood center, three collection facilities, and five hospitals, we found a cost advantage of the multi-product model over an uncoordinated model where the red blood cell and platelet supply chains are considered separately. Further sensitivity analyses indicated that the cost savings of the multi-product model mainly come from variations in the number of whole blood donors. These findings suggest that healthcare managers are able to see tremendous improvement in cost efficiency by considering red blood cell and platelet supply chains as a whole, especially with more whole blood donations and a higher percentage of whole blood derived platelets pooled for transfusion.

Balancing control and autonomy in master surgery scheduling: Benefits of ICU quotas for recovery units

When scheduling surgeries in the operating theater, not only the resources within the operating theater have to be considered but also those in downstream units, e.g., the intensive care unit and regular bed wards of each medical specialty. We present an extension to the master surgery schedule, where the capacity for surgeries on ICU patients is controlled by introducing downstream-dependent block types – one for both ICU and ward patients and one where surgeries on ICU patients must not be performed. The goal is to provide better control over post-surgery patient flows through the hospital while preserving each medical specialty’s autonomy over its operational surgery scheduling. We propose a mixed-integer program to determine the allocation of the new block types within either a given or a new master surgery schedule to minimize the maximum workload in downstream units. Using a simulation model supported by seven years of data from the University Hospital Augsburg, we show that the maximum workload in the intensive care unit can be reduced by up to 11.22% with our approach while maintaining the existing master surgery schedule. We also show that our approach can achieve up to 79.85% of the maximum workload reduction in the intensive care unit that would result from a fully centralized approach. We analyze various hospital setting instances to show the generalizability of our results. Furthermore, we provide insights and data analysis from the implementation of a quota system at the University Hospital Augsburg.

Bibliometric analysis of cancer care operations management: current status, developments, and future directions

Around the world, cancer care services are facing many operational challenges. Operations management research can provide important solutions to these challenges, from screening and diagnosis to treatment. In recent years, the growth in the number of papers published on cancer care operations management (CCOM) indicates that development has been fast. Within this context, the objective of this research was to understand the evolution of CCOM through a comprehensive study and an up-to-date bibliometric analysis of the literature. To achieve this aim, the Web of Science Core Collection database was used as the source of bibliographic records. The data-mining and quantitative tools in the software Biblioshiny were used to analyze CCOM articles published from 2010 to 2021. First, a historical analysis described CCOM research, the sources, and the subfields. Second, an analysis of keywords highlighted the significant developments in this field. Third, an analysis of research themes identified three main directions for future research in CCOM, which has 11 evolutionary paths. Finally, this paper discussed the gaps in CCOM research and the areas that require further investigation and development.

Hospital volume allocation: integrating decision maker and patient perspectives

Planning problems in healthcare systems have received greater attention in the last decade, especially because of the concerns recently raised about the scattering of surgical interventions among a wide number of different facilities that can undermine the quality of the outcome due to the volume-outcome association. In this paper, an approach to plan the amount of surgical interventions that a facility has to perform to assure a low adjusted mortality rate is proposed. The approach explicitly takes into account the existing interaction among patients’ choices and decision makers’ planning decisions. The first objective of the proposed approach is to find a solution able to reach quality in health outcomes and patients’ adherence. The second objective is to investigate the difference among solutions that are identified as optimal by either only one of the actors’ perspective, i.e., decision makers and patients, or by considering both the perspectives simultaneously. Following these objectives, the proposed approach is applied to a case study on Italian colon cancer interventions performed in 2014. Results confirm a variation in the hospital planned volumes when considering patients’ behaviour together with the policy maker plan, especially due to personal preferences and lack of information about hospital quality.

Decision support algorithms for optimizing surgery start times considering the performance variation

In this paper, we consider a stochastic optimization model for a surgical scheduling problem with a single operating room. The goal is to determine the optimal start times of multiple elective surgeries within a single day. The term "optimal" is defined as the largest surgically related utility value while achieving a given threshold defined by the performance variation of a reference solution. The optimization problem is analytically intractable because it involves quantities such as expectation and variance formulations. This implies that traditional mathematical programming techniques cannot be directly applied. We propose a decision support algorithm, which is a fully sequential procedure using variance screening in the first phase, and then employing multiple attribute utility theory to select the best solution in the second phase. The numerical experiments show that the proposed algorithm can find a promising solution in a reasonable amount of time.

Stroke care networks and the impact on quality of care

Lack of rapidly available neurological expertise, especially in rural areas, is one of the key obstacles in stroke care. Stroke care networks attempt to address this challenge by connecting hospitals with specialized stroke centers, stroke units, and hospitals of lower levels of care. While the benefits of stroke care networks are well-documented, travel distances are likely to increase when patients are transferred almost exclusively between members of the same network. This is particularly important for patients who require mechanical thrombectomy, an increasingly employed treatment method that requires equipment and expertise available in specialized stroke centers. This study aims to analyze the performance of the current design of stroke care networks in Bavaria, Germany, and to evaluate the improvement potential when the networks are redesigned to minimize travel distances. To this end, we define three fundamental criteria for assessing network design performance: 1) average travel distances, 2) the populace in the catchment area relative to the number of stroke units, and 3) the ratio of stroke units to lower-care hospitals. We generate several alternative stroke network designs using an analytical approach based on mathematical programming and clustering. Finally, we evaluate the performance of the existing networks in Bavaria via simulation. The results show that the current network design could be significantly improved concerning the average travel distances. Moreover, the existing networks are unnecessarily imbalanced when it comes to their number of stroke units per capita and the ratio of stroke units to lower-care hospitals.

A two-phase approach for the Radiotherapy Scheduling Problem

The Radiotherapy Scheduling Problem (RTSP) focuses on optimizing the planning of radiotherapy treatment sessions for cancer patients. In this paper, we propose a two-phase approach for the RTSP. In the first phase, radiotherapy sessions are assigned to specific linear accelerators (linacs) and days. The second phase then decides the sequence of patients on each day/linac and the specific appointment times. For the first phase, an Integer Linear Programming (IP) model is proposed and solved using CPLEX. For the second phase, a Mixed Integer Linear Programming (MIP) and a Constraint Programming (CP) model are proposed. The test data is generated based on real data from CHUM, a large cancer center in Montréal, Canada, with an average of 3,500 new patients and 40,000 radiotherapy treatments per year. The results show that in the second phase, CP is better at finding good solutions quickly while MIP is better at closing optimality gaps with more run time. Lastly, a simulation is conducted to evaluate the impact of different scheduling strategies on the outcome of the scheduling. Preliminary results show that batch scheduling reduces patients' waiting time and overdue time.

A two-stage robust optimization approach for the master surgical schedule problem under uncertainty considering downstream resources

This paper addresses a planning decision for operating rooms (ORs) that aim at supporting hospital management. Focusing on elective patients, we determined the master surgical schedule (MSS) on a one-week time horizon. We assigned the specialties to available sessions and allocated surgeries to them while taking into consideration the priorities of the outpatients in the ambulatory surgical discipline. Surgeries were selected from the waiting lists according to their priorities. The proposed approach considered operating theater (OT) restrictions, patients' priorities and accounted for the availability of both intensive care unit (ICU) beds and post-surgery beds. Since the management decisions of hospitals are usually made in an uncertain environment, our approach considered the uncertainty of surgery duration and availability of ICU bed. Two robust optimization approaches that kept the model computationally tractable are described and applied to deal with uncertainty. Computational results based on a medium-sized French hospital archives have been presented to compare the robust models to the deterministic counterpart and to demonstrate the price of robustness.

The Waiting Game - How Cooperation Between Public and Private Hospitals Can Help Reduce Waiting Lists

Prolonged waiting to access health care is a primary concern for nations aiming for comprehensive effective care, due to its adverse effects on mortality, quality of life, and government approval. Here, we propose two novel bargaining frameworks to reduce waiting lists in two-tier health care systems with local and regional actors. In particular, we assess the impact of 1) trading patients on waiting lists among hospitals, the 2) introduction of the role of private hospitals in capturing unfulfilled demand, and the 3) hospitals’ willingness to share capacity on the system performance. We calibrated our models with 2008–2018 Chilean waiting list data. If hospitals trade unattended patients, our game-theoretic models indicate a potential reduction of waiting lists of up to 37%. However, when private hospitals are introduced into the system, we found a possible reduction of waiting lists of up to 60%. Further analyses revealed a trade-off between diagnosing unserved demand and the additional expense of using private hospitals as a back-up system. In summary, our game-theoretic frameworks of waiting list management in two-tier health systems suggest that public–private cooperation can be an effective mechanism to reduce waiting lists. Further empirical and prospective evaluations are needed.

High Throughput Screening for Drug Discovery and Virus Detection

BACKGROUND

High throughput screening systems are automated labs for the analysis of many biochemical substances in the drug discovery and virus detection process. This paper was motivated by the problem of automating testing for viruses and new drugs using high throughput screening systems. The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the turn of 2019-2020 presented extradentary challenges to public health. Existing approaches to test viruses and new drugs do not use optimal schedules and are not efficient.

OBJECTIVE

The scheduling of activities performed by various resources in a high throughput screening system affects its efficiency, throughput, operations cost, and quality of screening. This study aims to minimize the total screening (flow) time and ensure the consistency and quality of screening.

METHODS

This paper develops innovative mixed integer models that efficiently compute optimal schedules for screening many microplates to identify new drugs and determine whether samples contain viruses. The methods integrate job-shop and cyclic scheduling. Experiments are conducted for a drug discovery process of screening an enzymatic assay and a general process of detecting SARS-CoV-2.

RESULTS

The method developed in this article can reduce screening time by as much as 91.67%.

CONCLUSION

The optimal schedules for high throughput screening systems greatly reduce the total flow time and can be computed efficiently to help discover new drugs and detect viruses.